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This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.

To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.

This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.

The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.

A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.

The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.

Postneonatal mortality (PNM), which differs from infant and perinatal mortality, has been observed in the past 25 years with respect to the health outcomes of children. While infant and perinatal mortality have been well-evaluated regarding racial differentials, there are no substantial data on PNM in this perspective. The purpose of this study was to assess whether or not social determinants of health adversely affect racial/ethnic PNM differentials in the USA.

A cross-sectional, nonexperimental epidemiologic study design was used to assess race as an exposure function of PNM using Cohort Linked Birth/Infant Death Data (2013). The outcome variable assessed PNM, while the main independent variables were race, social demographic variables (i.e. sex and age) and social determinants of health (i.e. marital status and maternal education). The chi-square statistic was used to assess the independence of variables by race, while the logistic regression model was used to assess the odds of PNM by race and other confounding variables.

During 2013, there were 4,451 children with PNM experience. The cumulative incidence of PNM was 23.6% (n = 2,795) among white infants, 24.3% (n = 1,298) among Black/African-Americans (AA) and 39.5% (n = 88) were American-Indian infants (AI), while 21.3% (n = 270) were multiracial, χ² (3) = 35.7, p < 0.001. Racial differentials in PNM were observed. Relative to White infants, PNM was two times as likely among AI, odds ratio (OR) 2.11 (95% confidence interval [CI] 1.61, 2.78). After controlling for the confounding variables, the burden of PNM persisted among AI, although slightly marginalized, adjusted odds ratio (aOR) 1.70, (99% CI 1.10, 2.65).

In a representative sample of US children, there were racial disparities in PNM infants who are AI compared to their white counterparts, illustrating excess mortality. These findings suggest the need to allocate social and health resources in transforming health equity in this direction.

Gender and geographical imbalance in production and impact levels is a pressing issue in global knowledge production. Within Health Sciences, while some studies found stark gender and geographical biases and inequalities, others found little empirical evidence of this marginalization. The purpose of the study is to clear the ambiguity concerning the topic.

Based on a comprehensive and systematic analysis of Health Sciences research data downloaded from the Scival (Scopus/Scimago) database from 2017 to 2020 (n = 7,990), this study first compares gender representation in research productivity, as well as differences in terms of citation per document, citations per document view and view per document scores according to geographical location. Additionally, the study clarifies whether there is a geographic bias in productivity and impact measures (i.e. citation per document, citations per document view and view per document) moderated by gender.

Results indicate that gender inequalities in productivity are systematic at the overall disciplinary, as well as the subfield levels. Findings also suggest statistically significant geographical differences in citation per document, citations per document view, and view per document scores, and interaction effect of gender over the relation between geography and (1) the number of citations per view and (2) the number of views per document.

This study contributes to scientometric studies in health sciences by providing insightful findings about the geographical and gender bias in productivity and impact across world regions.

The purpose of this research study is to empirically investigate the Figma prototyping technology adoption factors among entrepreneurship and innovation libraries for providing support to startups by developing and evolving the prototype solutions in collaboration with health libraries.

This study uses the technology adoption model (TAM) as a framework and the partial least squares structural equation modeling (PLS-SEM) method of structural equation modeling (SEM) using SmartPLS 3.2.9 software version to investigate the prototyping adoption factors among entrepreneurship and innovation libraries for rural health innovations. A total of 40 libraries, spread over 16 entrepreneurship and innovation libraries, participated in this survey, including participants from Europe (35%), Asia (15%) and USA (50%).

The findings show that previous experience, social impact, brand image and system quality have a significant positive impact on entrepreneurship and innovation libraries' perceived usefulness (PU) of prototyping technology. Perceived ease of use of prototype technology is positively influenced by usability, training materials and documentation, experience and self-efficacy. Together, perceived usefulness and perceived ease of use have a significant influence on behavioural intention. Behavioural intention is positively impacted by minimal investment and shallow learning curve. Technology adoption is furthered by behavioural intention. The control variables, for instance location, gender and work experience (as librarian), were found not having any impact on Figma technology adoption.

Through strategic partnerships with other libraries (including health libraries), policymakers, and technology providers, the adoption of prototype technology can be further accelerated. The important ramifications for policymakers, technology providers, public and entrepreneurship and innovation libraries to create a self-reliant innovation ecosystem to foster rural health innovation based on entrepreneurship are also listed in the article.

This research is distinctive since it integrates several areas of study, including entre, advances in rural healthcare and libraries. A novel idea that hasn't been thoroughly investigated is the collaboration between entrepreneurship and innovation libraries and health libraries for supporting businesses. This study offers insights into the factors that drive technology adoption and offers practical advice for policymakers and technology providers. It also advances understanding of the adoption of Figma prototyping technology among libraries for rural health innovation. Overall, this study provides a novel viewpoint on the nexus between different disciplines, showing the opportunity for cooperation and innovation in favour of rural health.

The COVID-19 pandemic provides researchers and practitioners with an opportunity to examine the effect of emergency policing on public trust in the police and augment our understanding. Therefore, the primary purpose of this study was to examine the effect of police enforcement of COVID-19 health measures on public trust in the police in Ghana.

A multivariate binary logistic regression was utilized to assess the effect of police enforcement of COVID-19 health measures on public trust in the police in Ghana using national representative data.

Our analysis suggests that emergency policing positively influences public trust in the police in Ghana. Additionally, we observed that police-related issues such as corruption and professionalism, as well as demographic factors of the public, influence trust in the police. These observations are helpful for emergency policing and policy development in Ghana.

This study is unique because it uses national representative data to assess the effect of police enforcement of COVID-19 health measures on public trust in the police in Ghana. Furthermore, this study is among the first or among the few from Ghana and the sub-region to examine the nexus between health emergencies and policing.

This study aims to assess the effect of water variation on bioethanol production from cassava peels (CP) using Saccharomyces cerevisiae yeast as the ethanologenic agent.

The milled CP was divided into three treatment groups in a small-scale flask experiment where each 20 g CP was subjected to two-stage hydrolysis. Different amount of water was added to the fermentation process of CP. The fermented samples were collected every 24 h for various analyses.

The results of the fermentation revealed that the highest ethanol productivity and fermentation efficiency was obtained at 17.38 ± 0.30% and 0.139 ± 0.003 gL⁻¹ h⁻¹. The study affirmed that ethanol production was increased for the addition of water up to 35% for the CP hydrolysate process.

The finding of this study demonstrates that S. cerevisiae is the key player in industrial ethanol production among a variety of yeasts that produce ethanol through sugar fermentation. In order to design truly sustainable processes, it should be expanded to include a thorough analysis and the gradual scaling-up of this process to an industrial level.

This paper is an original research work dealing with bioethanol production from CP using S. cerevisiae microbe.

1. Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

2. Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

3. Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

4. Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.

The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.

This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.

This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.

This study, informed by the Situational Crisis Communication Theory, aims to suggest two primary response strategies that can be used for effective internal crisis communication during a pandemic situation, such as COVID-19. The effect of base response strategies on employees' perceptions of communication quality, leadership and relational outcomes were investigated.

An online survey of full-time employees in the United States was conducted.

The findings showed that for an instructing information strategy, not all types of information were equally associated with positive employee responses in terms of perceived quality of internal communication related to the COVID-19 pandemic and transformational leadership. Specific information that employees need to know in order to safely perform daily tasks, such as organizational protocols and thorough preparation, seem to be the most needed and desired information. Adjusting information was positively associated with employee perceptions of internal communication quality and perceptions of CEO leadership. Employees' perceived quality of internal communication affected by the base crisis response strategies were positively correlated with perceptions of transformational leadership and relational outcomes (i.e. employee trust in the organization, employee perceptions of the organization's commitment to relationships with employees, employee support for organizational decision-making related to COVID-19).

This study presents important theoretical and practical insights through an interdisciplinary approach that applies the theoretical framework and relationship-oriented outcomes of public relations to public health crisis situations.

Building supply chain (SC) resilience has become a priority for many organizations, following a global increase in disruptive events. While management accounting and control (MAC) systems play a supportive role in supply chain management (SCM) decisions, little is known about the contributions offered to resilience decisions in service organizations. The purpose of this study is to examine the performance implications of MCS's impact on proactive and reactive resilience of healthcare supply chains.

This study conducted a survey of 127 public health managers via structural equation modeling. The partial least squares version 3.3.3 was used.

The results show a statistically positive impact of MAC dimensions on proactive and reactive resilience, which in turn impacts the quality, delivery speed and cost effectiveness of the health SC. However, the integration dimension had an insignificant effect on reactive resilience but a positive effect on proactive resilience.

This study examined the performance implications of MAC system dimensions and proactive and reactive resilience on operational performance in health SCs, using empirical data from only one country. Thus, generalizing the findings to include other jurisdictions may be impossible.

Healthcare managers in public health facilities should embrace the four MAC dimensions (except the integrated dimension in reactive resilience) to support information generation in SC resilience decisions.

Perhaps, the first to provide preliminary empirical evidence on the interactive effect of proactive and reactive resilience and MAC dimensions in terms of broad scope, timeliness, integration and aggregation on health SC operational performance under disruption, in the context of an emerging economy.